Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Making large-scale support vector machine learning practical
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Unsupervised multi-label text classification using a world knowledge ontology
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
The impact of conceptualization on text classification
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Conceptualization Effects on MEDLINE Documents Classification Using Rocchio Method
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Mapping semantic knowledge for unsupervised text categorisation
ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
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An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents.